Chronic Kidney Disease Risk Prediction Using Machine Learning Techniques

Document Type : Research Paper

Authors

1 Computer Science and Engineering, Vasavi College of Engineering, Hyderabad, Telangana, India.

2 School of Computing and Information Technology, REVA University, Bangalore (North), Karnataka, India.

3 Computing Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, India.

4 Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India.

5 Computer Science and Engineering MLR Institute of Technology, Dundigal, Hyderabad, Telangana, India.

6 School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India.

7 School of Computing Science and Artificial Intelligence, SR University, Warangal-506371, Telangana, India.

Abstract

In healthcare, a diagnosis is reached after a thorough physical assessment and analysis of the patient's medicinal history, as well as the utilization of appropriate diagnostic tests and procedures. 1.7 million People worldwide lose their lives every year due to complications from chronic kidney disease (CKD). Despite the availability of other diagnostic approaches, this investigation relies on machine learning because of its superior accuracy. Patients with chronic kidney disease (CKD) who experience health complications like high blood pressure, anemia, mineral-bone disorder, poor nutrition, acid abnormalities, and neurological-complications may benefit from timely and exact recognition of the disease's levels so that they can begin treatment with the most effective medications as soon as possible. Several works have been investigated on the early recognition of CKD utilizing machine-learning (ML) strategies. The accuracy of stage anticipations was not their primary concern. Both binary and multiclass classification methods have been used for stage anticipation in this investigation. Random-Forest (RF), Support-Vector-Machine (SVM), and Decision-Tree (DT) are the prediction models employed. Feature-selection has been carried out through scrutiny of variation and recursive feature elimination utilizing cross-validation (CV). 10-flod CV was utilized to assess the models. Experiments showed that RF utilizing recursive feature removal with CV outperformed SVM and DT.

Keywords

References

Ahmed, T. I., Bhola, J., Shabaz, M., Singla, J., Rakhra, M., More, S., & Samori, I. A. (2022). Fuzzy logic-based systems for the diagnosis of chronic kidney disease. BioMed Research International, 2022.
Ashreetha, B., Devi, M. R., Kumar, U. P., Mani, M. K., Sahu, D. N., & Reddy, P. C. S. (2022). Soft optimization techniques for automatic liver cancer detection in abdominal liver images. International journal of health sciences, 6.
Aswathy, R. H., Suresh, P., Sikkandar, M. Y., Abdel-Khalek, S., Alhumyani, H., Saeed, R. A., & Mansour, R. F. (2022). Optimized tuned deep learning model for chronic kidney disease classification. Comput. Mater. Contin, 70, 2097-2111.
Barua, N. (2020). Computational Study on Interfacial Properties of Boron Nitride Nanosheets and Its Relevance for Application in Ethylene Glycol Removal from Water (Doctoral dissertation, North Carolina Agricultural and Technical State University).
Baskar, S., Nandhini, I., Prasad, M. L., Katale, T., Sharma, N., & Reddy, P. C. S. (2023, November). An Accurate Prediction and Diagnosis of Alzheimer’s Disease using Deep Learning. In 2023 IEEE North Karnataka Subsection Flagship International Conference (NKCon) (pp. 1-7). IEEE.
Colli, V. A., González-Rocha, A., Canales, D., Hernández-Alcáraz, C., Pedroza, A., Pérez-Chan, M. & Denova-Gutierrez, E. (2022). Chronic kidney disease risk prediction scores assessment and development in Mexican adult population. Frontiers in Medicine, 9, 903090.
Deivasigamani, S., Rani, A. J. M., Natchadalingam, R., Vijayakarthik, P., Kumar, G. B. S., & Reddy, P. C. S. (2023, August). Crop Yield Prediction Using Deep Reinforcement Learning. In 2023 Second International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON) (pp. 137-142). IEEE.
Deo, R., Dubin, R. F., Ren, Y., Murthy, A. C., Wang, J., Zheng, H. & Ganz, P. (2023). Proteomic cardiovascular risk assessment in chronic kidney disease. European Heart Journal, 44(23), 2095-2110.
Ebiaredoh-Mienye, S. A., Swart, T. G., Esenogho, E., & Mienye, I. D. (2022). A machine learning method with filter-based feature selection for improved prediction of chronic kidney disease. Bioengineering, 9(8), 350.
Hui, M., Ma, J., Yang, H., Gao, B., Wang, F., Wang, J. &  Zhao, M. (2023). ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study. Journal of Clinical Medicine, 12(4), 1504.
Kumar, A., Satheesha, T. Y., Salvador, B. B. L., Mithileysh, S., & Ahmed, S. T. (2023). Augmented Intelligence enabled Deep Neural Networking (AuDNN) framework for skin cancer classification and prediction using multi-dimensional datasets on industrial IoT standards. Microprocessors and Microsystems, 97, 104755.
Kumar, G. R., Reddy, R. V., Jayarathna, M., Pughazendi, N., Vidyullatha, S., & Reddy, P. C. S. (2023, May). Web application based Diabetes prediction using Machine Learning. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
Lambert, J. R., & Perumal, E. (2022). Oppositional firefly optimization based optimal feature selection in chronic kidney disease classification using deep neural network. Journal of Ambient Intelligence and Humanized Computing, 13(4), 1799-1810.
Latha, S. B., Dastagiraiah, C., Kiran, A., Asif, S., Elangovan, D., & Reddy, P. C. S. (2023, August). An Adaptive Machine Learning model for Walmart sales prediction. In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT) (pp. 988-992). IEEE.
Lee, C. L., Liu, W. J., & Tsai, S. F. (2022). Development and validation of an insulin resistance model for a population with chronic kidney disease using a machine learning approach. Nutrients, 14(14), 2832.
LK, S. S., Ahmed, S. T., Anitha, K., & Pushpa, M. K. (2021, November). COVID-19 outbreak based coronary heart diseases (CHD) prediction using SVM and risk factor validation. In 2021 Innovations in Power and Advanced Computing Technologies (i-PACT) (pp. 1-5). IEEE.
Major, R. W., Cockwell, P., Nitsch, D., & Tangri, N. (2022). The next step in chronic kidney disease staging: individualized risk prediction. Kidney international, 102(3), 456-459.
Mamatha, B., Rashmi, D., Tiwari, K. S., Sikrant, P. A., Jovith, A. A., & Reddy, P. C. S. (2023, August). Lung Cancer Prediction from CT Images and using Deep Learning Techniques. In 2023 Second International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON) (pp. 263-267). IEEE.
Matsushita, K., Kaptoge, S., Hageman, S. H., Sang, Y., Ballew, S. H., Grams, M. E., ... & Coresh, J. (2023). Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP. European journal of preventive cardiology, 30(1), 8-16.
Mondol, C., Shamrat, F. J. M., Hasan, M. R., Alam, S., Ghosh, P., Tasnim, Z. & Ibrahim, S. M. (2022). Early prediction of chronic kidney disease: A comprehensive performance analysis of deep learning models. Algorithms, 15(9), 308.
Muthappa, K. A., Nisha, A. S. A., Shastri, R., Avasthi, V. & Reddy, P. C. S. (2023). Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs. Applied Nanoscience, 1-10.
Raj, A., Tollens, F., Caroli, A., Nörenberg, D., & Zöllner, F. G. (2023). Automated prognosis of renal function decline in ADPKD patients using deep learning. Zeitschrift für Medizinische Physik.
Rao, K. R., Prasad, M. L., Kumar, G. R., Natchadalingam, R., Hussain, M. M., & Reddy, P. C. S. (2023, August). Time-Series Cryptocurrency Forecasting Using Ensemble Deep Learning. In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT) (pp. 1446-1451). IEEE.
Saha, I., Gourisaria, M. K. & Harshvardhan, G. M. (2022). Classification System for Prediction of Chronic Kidney Disease Using Data Mining Techniques. In Advances in Data and Information Sciences: Proceedings of ICDIS 2021 (pp. 429-443). Singapore: Springer Singapore.
Sampath, S., Parameswari, R., Prasad, M. L., Kumar, D. A., Hussain, M. M. & Reddy, P. C. S. (2023, December). Ensemble Nonlinear Machine Learning Model for Chronic Kidney Diseases Prediction. In 2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon) (pp. 1-6). IEEE.
Sharma, R., Shrivastava, S., Singh, S. K., Kumar, A., Singh, A. K. & Saxena, S. (2023). EnDL-HemoLyt: Ensemble Deep Learning-based Tool for Identifying Therapeutic Peptides with Low Hemolytic Activity. IEEE Journal of Biomedical and Health Informatics.
Sucharitha, Y., Reddy, P. C. S. & Chitti, T. N. (2023, July). Deep learning based framework for crop yield prediction. In AIP Conference Proceedings,2548 (1). AIP Publishing.
Sucharitha, Y., Reddy, P. C. S., & Suryanarayana, G. (2023). Network Intrusion Detection of Drones Using Recurrent Neural Networks. Drone Technology: Future Trends and Practical Applications, 375-392.
Suneel, S., Balaram, A., Amina Begum, M., Umapathy, K., Reddy, P. C. S., & Talasila, V. (2024). Quantum mesh neural network model in precise image diagnosing. Optical and Quantum Electronics, 56(4), 559.
Teju, V., Sowmya, K. V., Yuvanika, C., Saikumar, K., & Krishna, T. B. D. S. (2021, December). Detection of diabetes melittus, kidney disease with ML. In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) (pp. 217-222). IEEE.
Wang, H., Bowe, B., Cui, Z., Yang, H., Swamidass, S. J., Xie, Y., & Al-Aly, Z. (2022). A deep learning approach for the estimation of glomerular filtration rate. IEEE Transactions on NanoBioscience, 21(4), 560-569.
 
Journal of Information Technology Management
Volume 16, Issue 1
2024
Pages 118-134

Files

Share

How to cite

Statistics